By now we see how the gig economy and the future of self-driving cars are changing the transportation industry. In addition, the ever-increasing distracted driving due to mobile phone use is an immediate and costly driver risk. Both short and long-term, these changes necessitate the need for a more relevant pricing model.

So how do we price insurance rates for this new normal? Insurance companies have long relied on demographic and credit data, but telematics data that measures drivers’ actual behaviors instead of reported behaviors, is the next step in understanding customers and predicting future losses.

Arity uses these five components for accurate telematics pricing:

Predicts future insurance losses

A telematics model should reflect actual insurance risk after all this is the business we are in. We shouldn’t just model whether a crash event could take place. We must consider if the driving behaviors are actually causing insurance losses (frequency) and what the cost is to insurance (severity).

For example, our driving data looks at driving behaviors that have proven to result in costly incidents, like messing with your phone while speeding

Accounts for traditional factors

If driving behaviors are analyzed without adjustment for traditional rating factors, the correlation between these variables could be double-counted. For example, younger drivers tend to have more extreme driving behaviors than other demographics. To some extent, traditional rating plans are already accounting for this. We need to be sure not to double count the effect of driving inexperience and isolate the impact of the actual driving behaviors.

Models by coverage

Collision claims tend to be small but frequent, whereas bodily injury claims are infrequent but severe. Because the differences in resulting claims and severity varies significantly by coverage, modeling the impact of driving behaviors on insurance losses separately by coverage will enhance the predictive power of the overall rating plan. Arity has found that the variables we use change by coverage. For example, speed relative to the pace of the road might be more impactful for collision than comprehensive.

Adapts to sensor type

The truth is that hardware and mobile sensors collect different data. A hard fixed device may be able to read robust vehicle-specific data such as odometer and even noise level, while a mobile sensor can capture individual behaviors such as phone use and different modes of transportation. Even when the actual data elements are the same (e.g., GPS), the actual sensors’ quality and consistency vary. Normalization can be applied but in robust data sets it is proven that the best predictive power can only be obtained when the data is modeled by generalized sensor type. You’ll see new variables emerge and factors change.

Utilizes meaningful and actionable factors

The overwhelming majority of telematics programs deployed by insurance companies today include a consumer facing experience and pricing adjustment based on actual driving behavior. Therefore it’s critical that the metrics used in telematics modeling are meaningful and relevant to the driver. Drivers need to be able to understand what is driving their pricing and make appropriate adjustments in their behavior.

Put together, these five components are more predictive of actual risk. Not only that, they benefit insurance customers. Telematics data can help us customize policies for each individual driver, avoid claims, and save money (by incentivizing good driving behaviors, etc.). What’s more, being transparent and making meaning from the data builds trust with their insurer and gives them opportunities to improve their driving safety.

Using telematics data for pricing not only sets up carriers for the ‘new normal’, it positions them for ongoing opportunities to transform with their industry as more data becomes available for accurately predicting and pricing risk — it’s Insurance 2.0

Gina Minick

Gina Minick, Director of Product – Insurance Solutions, loves tackling tough problems while focusing on developing a strategy to put new products in the market. She loves to travel and challenges herself to find the oldest restaurant in every city she visits. Her best meal ever – the oldest restaurant in Paris, La Petite Chaise.